High resolution images of selected portions of a celestial body's surface have become a product desired and used by government agencies, corporations, and individuals. For instance, many consumer products in common use today include images of the Earth's surface, such as Google® Earth. Various types of remote sensing image collection platforms may be employed, including aircraft, earth-orbiting satellites, and the like.
In the case of a consumer digital camera, for instance, an image sensor is generally arranged in an area array (e.g., 3,000 rows of 3,000 pixels each, or 9,000,000 total pixels) which collects the image area in a single “snapshot.” However, satellite-based imaging often functions on the “push-broom scanning” principle whereby each image sensor includes a relatively small number of rows (e.g., a couple) of a great number of pixels (e.g., 50,000) in each row. Each row of pixels is scanned across the earth to build an image line by line, and the width of the image is the product of the number of pixels in the row times the pixel size or resolution (e.g., 50,000 pixels at 0.5 meter ground resolution produces an image that is 25,000 meters wide). The length of the image is controlled by the scan duration (i.e. number of lines), which is typically settable for each image collected. The resolution of satellite images varies depending on factors such as the particular instrumentation utilized, the altitude of the satellite's orbit, and the like.
To allow for the extraction of additional information from the radiance received at a satellite after being reflected from the Earth's surface (which may include atmospheric effects such as from aerosols, clouds, etc.), multi-spectral imaging may be employed. Specifically, multi-spectral imaging captures image data at specific frequencies or wavelengths across the electromagnetic spectrum, including those in the visible light range as well as those beyond or outside of the visible light range (e.g., near infrared (NIR), short wave infrared (SWIR), far infrared (FIR), etc.). For instance, a satellite may have one image sensor (e.g., radiometer) that is sensitive to wavelengths (e.g., high resolution data) across only a first spectral band (e.g., the visible light band, 0.38-0.75 μm) in addition to one or more additional image sensors that are sensitive to wavelengths only across other spectral bands (e.g., NIR, 0.75-1.4 μm; SWIR, 1.4-3 μm; etc.).
Due to the nature of image acquisition, a number of geospatial images may be pieced together to form an orthomosaic of a collection of geospatial images that cover a larger geographic area than may be feasibly covered with a single acquired image. In this regard, it may be appreciated that the images that form such an orthomosaic may be acquired at different times or may be acquired using different collection techniques or parameters. In situations where more than one image is available for a given region of interest on the ground, it may be desirable to use the most recent image. Various artifacts can be introduced when multiple separate images are combined into an orthomosaic.
Up until recently, orthomosaic generation has always included manual selection of images by a human operator. Generally, the human operator is tasked with reviewing all available images for an area of interest and choosing images for inclusion in the orthomosaic utilizing what the human operator subjectively determines to be the “best” source images. The subjective determinations of the human operator are often guided by a principle that it is preferential to include as few images in the orthomosaic as possible. In turn, an orthomosaic may be generated utilizing the human-selected images to form the orthomosaic. As may be appreciated, this human operator-centric process may be time consuming and costly. Moreover, the image selection is subjective to the human user.
It is against this background that the techniques disclosed herein have been developed.